{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 首先 import 必要的模块\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "0          6                           148              72   \n",
       "1          1                            85              66   \n",
       "2          8                           183              64   \n",
       "3          1                            89              66   \n",
       "4          0                           137              40   \n",
       "\n",
       "   Triceps_skin_fold_thickness  serum_insulin   BMI  \\\n",
       "0                           35              0  33.6   \n",
       "1                           29              0  26.6   \n",
       "2                            0              0  23.3   \n",
       "3                           23             94  28.1   \n",
       "4                           35            168  43.1   \n",
       "\n",
       "   Diabetes_pedigree_function  Age  Target  \n",
       "0                       0.627   50       1  \n",
       "1                       0.351   31       0  \n",
       "2                       0.672   32       1  \n",
       "3                       0.167   21       0  \n",
       "4                       2.288   33       1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据\n",
    "# path to where the data lies\n",
    "dpath = './pima-indians-diabetes.csv'\n",
    "train = pd.read_csv(dpath)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 768 entries, 0 to 767\n",
      "Data columns (total 9 columns):\n",
      "pregnants                       768 non-null int64\n",
      "Plasma_glucose_concentration    768 non-null int64\n",
      "blood_pressure                  768 non-null int64\n",
      "Triceps_skin_fold_thickness     768 non-null int64\n",
      "serum_insulin                   768 non-null int64\n",
      "BMI                             768 non-null float64\n",
      "Diabetes_pedigree_function      768 non-null float64\n",
      "Age                             768 non-null int64\n",
      "Target                          768 non-null int64\n",
      "dtypes: float64(2), int64(7)\n",
      "memory usage: 54.1 KB\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pregnants</th>\n",
       "      <th>Plasma_glucose_concentration</th>\n",
       "      <th>blood_pressure</th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>serum_insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Diabetes_pedigree_function</th>\n",
       "      <th>Age</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "      <td>768.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.845052</td>\n",
       "      <td>120.894531</td>\n",
       "      <td>69.105469</td>\n",
       "      <td>20.536458</td>\n",
       "      <td>79.799479</td>\n",
       "      <td>31.992578</td>\n",
       "      <td>0.471876</td>\n",
       "      <td>33.240885</td>\n",
       "      <td>0.348958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.369578</td>\n",
       "      <td>31.972618</td>\n",
       "      <td>19.355807</td>\n",
       "      <td>15.952218</td>\n",
       "      <td>115.244002</td>\n",
       "      <td>7.884160</td>\n",
       "      <td>0.331329</td>\n",
       "      <td>11.760232</td>\n",
       "      <td>0.476951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.078000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>0.243750</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>117.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>0.372500</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>140.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>127.250000</td>\n",
       "      <td>36.600000</td>\n",
       "      <td>0.626250</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>17.000000</td>\n",
       "      <td>199.000000</td>\n",
       "      <td>122.000000</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>846.000000</td>\n",
       "      <td>67.100000</td>\n",
       "      <td>2.420000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        pregnants  Plasma_glucose_concentration  blood_pressure  \\\n",
       "count  768.000000                    768.000000      768.000000   \n",
       "mean     3.845052                    120.894531       69.105469   \n",
       "std      3.369578                     31.972618       19.355807   \n",
       "min      0.000000                      0.000000        0.000000   \n",
       "25%      1.000000                     99.000000       62.000000   \n",
       "50%      3.000000                    117.000000       72.000000   \n",
       "75%      6.000000                    140.250000       80.000000   \n",
       "max     17.000000                    199.000000      122.000000   \n",
       "\n",
       "       Triceps_skin_fold_thickness  serum_insulin         BMI  \\\n",
       "count                   768.000000     768.000000  768.000000   \n",
       "mean                     20.536458      79.799479   31.992578   \n",
       "std                      15.952218     115.244002    7.884160   \n",
       "min                       0.000000       0.000000    0.000000   \n",
       "25%                       0.000000       0.000000   27.300000   \n",
       "50%                      23.000000      30.500000   32.000000   \n",
       "75%                      32.000000     127.250000   36.600000   \n",
       "max                      99.000000     846.000000   67.100000   \n",
       "\n",
       "       Diabetes_pedigree_function         Age      Target  \n",
       "count                  768.000000  768.000000  768.000000  \n",
       "mean                     0.471876   33.240885    0.348958  \n",
       "std                      0.331329   11.760232    0.476951  \n",
       "min                      0.078000   21.000000    0.000000  \n",
       "25%                      0.243750   24.000000    0.000000  \n",
       "50%                      0.372500   29.000000    0.000000  \n",
       "75%                      0.626250   41.000000    1.000000  \n",
       "max                      2.420000   81.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.info()\n",
    "\n",
    "#查看数值型特征的基本统计量\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pregnants                         0\n",
      "Plasma_glucose_concentration      5\n",
      "blood_pressure                   35\n",
      "Triceps_skin_fold_thickness     227\n",
      "serum_insulin                   374\n",
      "BMI                              11\n",
      "Diabetes_pedigree_function        0\n",
      "Age                               0\n",
      "Target                            0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "NaN_col_names = ['Plasma_glucose_concentration','blood_pressure','Triceps_skin_fold_thickness','serum_insulin','BMI']\n",
    "train[NaN_col_names] = train[NaN_col_names].replace(0, np.NaN)\n",
    "print(train.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对缺失值较多的特征，新增一个特征，表示这个特征是否缺失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Triceps_skin_fold_thickness</th>\n",
       "      <th>Triceps_skin_fold_thickness_Missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>45.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Triceps_skin_fold_thickness  Triceps_skin_fold_thickness_Missing\n",
       "0                         35.0                                    0\n",
       "1                         29.0                                    0\n",
       "2                          NaN                                    1\n",
       "3                         23.0                                    0\n",
       "4                         35.0                                    0\n",
       "5                          NaN                                    1\n",
       "6                         32.0                                    0\n",
       "7                          NaN                                    1\n",
       "8                         45.0                                    0\n",
       "9                          NaN                                    1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#缺失值比较多，干脆就开一个新的字段，表明是缺失值还是不是缺失值\n",
    "train['Triceps_skin_fold_thickness_Missing'] = train['Triceps_skin_fold_thickness'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "train[['Triceps_skin_fold_thickness','Triceps_skin_fold_thickness_Missing']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x967cd68>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAELCAYAAADDZxFQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAGnpJREFUeJzt3XuQFeWd//H3Ry6CEUVgdJEBId42EJdBRzRGE2+/H4Ssikm0YJP1HkwVScwmsjGplCLRqiSbROOa6GK8YGJQ10RF10vUqKzZKCIiAi4lRn8yooKorC6iMn5/f/QzchyfGQ44PWdgPq+qU3P66ae7v+cMnM883X26FRGYmZm1tl2tCzAzs67JAWFmZlkOCDMzy3JAmJlZlgPCzMyyHBBmZpblgDAzsywHhJmZZTkgzMwsq2etC/goBg0aFMOHD691GWZmW5XHHnvslYio21S/rToghg8fzvz582tdhpnZVkXS/6umn3cxmZlZlgPCzMyyHBBmZpa1VR+DMDOrlXfffZempibWr19f61La1KdPH+rr6+nVq9cWLe+AMDPbAk1NTfTr14/hw4cjqdblfEhEsGbNGpqamhgxYsQWrcO7mMzMtsD69esZOHBglwwHAEkMHDjwI41wHBBmZluoq4ZDi49anwPCzMyyfAzCzKwDrFmzhqOOOgqAl156iR49elBXV3xZed68efTu3bvDt7lgwQJWrVrF+PHjO3zd4IDggGnX1rqELuOxfzmp1iWYbbUGDhzIwoULAZg+fTo77rgjZ599dtXLNzc306NHj83a5oIFC1i8eHFpAeFdTGZmJTvmmGM44IADGDVqFL/+9a8B2LBhA/379+cHP/gBY8eOZd68ecyZM4d9992Xww47jG984xtMnDgRgDfffJNTTjmFsWPHMmbMGG677TbeeustZsyYwXXXXUdDQwM33XRTh9dd2ghCUh9gLrB92s5NEXGepGuAzwJrU9dTImKhiqMpvwAmAOtS+4Ky6jMz6yyzZs1iwIABrFu3jsbGRr74xS/Sr18/1q5dy/77788FF1zAunXr2Gefffjzn//MsGHDOPHEE99ffsaMGYwfP55rrrmG1157jYMOOohFixZx7rnnsnjxYi6++OJS6i5zBPE2cGREjAYagPGSDk7zpkVEQ3osTG2fA/ZOjynAZSXWZmbWaS666CJGjx7Npz71KZqamnjmmWcA6N27N8cffzwAS5cuZd9992WPPfZAEpMnT35/+T/+8Y9ceOGFNDQ0cMQRR7B+/Xqef/750usubQQREQG8mSZ7pUe0s8hxwLVpuYcl9Zc0OCJeLKtGM7Oy3XvvvcydO5eHH36Yvn37cuihh77/3YS+ffu+fypq8dGXFxHccsst7Lnnnh9onzt3bnmFU/IxCEk9JC0EVgH3RMQjadaFkhZJukjS9qltCLCiYvGm1GZmttVau3YtAwYMoG/fvixZsoRHH30022/UqFEsW7aMFStWEBHccMMN788bN24cl1xyyfvTjz/+OAD9+vXjjTfeKK32UgMiIpojogGoB8ZK+iTwPeBvgQOBAcB3U/fcNzo+FKmSpkiaL2n+6tWrS6rczKxjfP7zn2fdunWMHj2aGTNmcNBBB2X77bDDDlx66aUcffTRHHbYYey+++7svPPOAJx33nmsW7eO/fbbj1GjRjF9+nQAjjzySJ544gnGjBmzdR2krhQRr0t6ABgfET9NzW9LuhpoOQ+sCRhasVg9sDKzrpnATIDGxsb2dlmZmdVEywc4FBfMu/vuu7P9Xn/99Q9MH3300SxbtoyI4Mwzz6SxsRGAj33sY1xxxRUfWr6urq7Um6aVNoKQVCepf3reFzga+G9Jg1ObgInA4rTIHOAkFQ4G1vr4g5l1J5dddhkNDQ2MHDmSt956i69+9as1rafMEcRgYJakHhRBdGNE3C7pT5LqKHYpLQS+lvrfQXGK63KK01xPLbE2M7MuZ9q0aUybNq3WZbyvzLOYFgFjMu1HttE/gKll1WNmZpvH36Q2M7MsB4SZmWU5IMzMLKvbX83VzKwjdPSVoau5uvJdd93FWWedRXNzM2eccQbnnHNOh9bgEYSZ2VaoubmZqVOncuedd7J06VJmz57N0qVLO3QbDggzs63QvHnz2Guvvfj4xz9O7969mTRpErfeemuHbsMBYWa2FXrhhRcYOnTjxSfq6+t54YUXOnQbDggzs61Q7uqvLVeG7SgOCDOzrVB9fT0rVmy8AHZTUxO77757h27DAWFmthU68MADefrpp3n22Wd55513uP766zn22GM7dBs+zdXMrANUc1pqR+rZsyeXXnop48aNo7m5mdNOO41Ro0Z17DY6dG1mZtZpJkyYwIQJE0pbv3cxmZlZlgPCzMyyHBBmZpblgDAzsywHhJmZZTkgzMwsy6e5mpl1gOdn7Neh6xt27pOb7HPaaadx++23s+uuu7J48eIO3T54BGFmttU65ZRTuOuuu0pbf2kBIamPpHmSnpC0RNL5qX2EpEckPS3pBkm9U/v2aXp5mj+8rNrMzLYFn/nMZxgwYEBp6y9zBPE2cGREjAYagPGSDgZ+DFwUEXsDrwGnp/6nA69FxF7ARamfmZnVSGkBEYU302Sv9AjgSOCm1D4LmJieH5emSfOPUkdfu9bMzKpW6jEIST0kLQRWAfcAzwCvR8SG1KUJGJKeDwFWAKT5a4GBZdZnZmZtKzUgIqI5IhqAemAs8Ilct/QzN1r40B0xJE2RNF/S/NWrV3dcsWZm9gGdcpprRLwu6QHgYKC/pJ5plFAPrEzdmoChQJOknsDOwKuZdc0EZgI0NjZ++JZKZmY1UM1pqR1t8uTJPPDAA7zyyivU19dz/vnnc/rpp296wSqVFhCS6oB3Uzj0BY6mOPB8P/Al4HrgZKDlLttz0vRf0vw/Re6eemZmBsDs2bNLXX+ZI4jBwCxJPSh2Zd0YEbdLWgpcL+kC4HHgytT/SuA3kpZTjBwmlVibmZltQmkBERGLgDGZ9r9SHI9o3b4eOKGseszMbPP4m9RmZluoq+8F/6j1OSDMzLZAnz59WLNmTZcNiYhgzZo19OnTZ4vX4Yv1mZltgfr6epqamujKp9v36dOH+vr6LV7eAWFmtgV69erFiBEjal1GqbyLyczMshwQZmaW5YAwM7MsB4SZmWU5IMzMLMsBYWZmWQ4IMzPLckCYmVmWA8LMzLIcEGZmluWAMDOzLAeEmZllOSDMzCzLAWFmZlkOCDMzy3JAmJlZVmkBIWmopPslPSVpiaSzUvt0SS9IWpgeEyqW+Z6k5ZKWSRpXVm1mZrZpZd5RbgPwnYhYIKkf8Jike9K8iyLip5WdJY0EJgGjgN2BeyXtExHNJdZoZmZtKG0EEREvRsSC9PwN4ClgSDuLHAdcHxFvR8SzwHJgbFn1mZlZ+zrlGISk4cAY4JHU9HVJiyRdJWmX1DYEWFGxWBOZQJE0RdJ8SfO78s3Czcy2dqUHhKQdgd8D34qI/wEuA/YEGoAXgZ+1dM0sHh9qiJgZEY0R0VhXV1dS1WZmVmpASOpFEQ7XRcQfACLi5Yhojoj3gCvYuBupCRhasXg9sLLM+szMrG1lnsUk4ErgqYj4eUX74IpuxwOL0/M5wCRJ20saAewNzCurPjMza1+ZZzF9GvhH4ElJC1Pb94HJkhoodh89B5wJEBFLJN0ILKU4A2qqz2AyM6ud0gIiIh4if1zhjnaWuRC4sKyazMysev4mtZmZZTkgzMwsywFhZmZZDggzM8tyQJiZWZYDwszMshwQZmaW5YAwM7MsB4SZmWU5IMzMLMsBYWZmWQ4IMzPLckCYmVmWA8LMzLIcEGZmluWAMDOzLAeEmZllVRUQku6rps3MzLYd7d5yVFIfYAdgkKRd2HgL0Z2A3UuuzczMamhTI4gzgceAv00/Wx63Ar9sb0FJQyXdL+kpSUsknZXaB0i6R9LT6ecuqV2SLpG0XNIiSft/1BdnZmZbrt2AiIhfRMQI4OyI+HhEjEiP0RFx6SbWvQH4TkR8AjgYmCppJHAOcF9E7A3cl6YBPgfsnR5TgMu2/GWZmdlH1e4uphYR8a+SDgGGVy4TEde2s8yLwIvp+RuSngKGAMcBh6dus4AHgO+m9msjIoCHJfWXNDitx8zMOllVASHpN8CewEKgOTUH0GZAtFp+ODAGeATYreVDPyJelLRr6jYEWFGxWFNqc0CYmdVAVQEBNAIj01/3m0XSjsDvgW9FxP9IarNrpu1D25M0hWIXFMOGDdvccszMrErVfg9iMfA3m7tySb0owuG6iPhDan5Z0uA0fzCwKrU3AUMrFq8HVrZeZ0TMjIjGiGisq6vb3JLMzKxK1QbEIGCppLslzWl5tLeAiqHClcBTEfHzillzgJPT85MpzohqaT8pnc10MLDWxx/MzGqn2l1M07dg3Z8G/hF4UtLC1PZ94EfAjZJOB54HTkjz7gAmAMuBdcCpW7BNMzPrINWexfTg5q44Ih4if1wB4KhM/wCmbu52zMysHNWexfQGGw8Y9wZ6Af8bETuVVZiZmdVWtSOIfpXTkiYCY0upyMzMuoQtupprRNwCHNnBtZiZWRdS7S6mL1RMbkfxvYjN/k6EmZltPao9i+mYiucbgOcoLo1hZmbbqGqPQfiUUzOzbqbaGwbVS7pZ0ipJL0v6vaT6soszM7PaqXYX09XA79j4pbavpLb/U0ZRVhvPz9iv1iV0GcPOfbLWJZjVXLVnMdVFxNURsSE9rgF8ISQzs21YtQHxiqSvSOqRHl8B1pRZmJmZ1Va1AXEacCLwEsX9Gb6Er5VkZrZNq/YYxA+BkyPiNSjuKw38lCI4zMxsG1TtCOLvWsIBICJepbhDnJmZbaOqDYjtJO3SMpFGENWOPszMbCtU7Yf8z4D/knQTxSU2TgQuLK0qMzOruWq/SX2tpPkUF+gT8IWIWFpqZWZmVlNV7yZKgeBQMDPrJrboct9mZrbtc0CYmVmWA8LMzLJKCwhJV6Wrvy6uaJsu6QVJC9NjQsW870laLmmZpHFl1WVmZtUpcwRxDTA+035RRDSkxx0AkkYCk4BRaZlfSepRYm1mZrYJpQVERMwFXq2y+3HA9RHxdkQ8CywHxpZVm5mZbVotjkF8XdKitAuq5dvZQ4AVFX2aUpuZmdVIZwfEZcCeQAPFVWF/ltqV6Ru5FUiaImm+pPmrV68up0ozM+vcgIiIlyOiOSLeA65g426kJmBoRdd6YGUb65gZEY0R0VhX53sWmZmVpVMDQtLgisnjgZYznOYAkyRtL2kEsDcwrzNrMzOzDyrtiqySZgOHA4MkNQHnAYdLaqDYffQccCZARCyRdCPFpTw2AFMjorms2szMbNNKC4iImJxpvrKd/hfiK8SamXUZ/ia1mZll+aY/Zl3UAdOurXUJXcZj/3JSrUvoljyCMDOzLAeEmZllOSDMzCzLAWFmZlkOCDMzy3JAmJlZlgPCzMyyHBBmZpblgDAzsywHhJmZZTkgzMwsywFhZmZZDggzM8tyQJiZWZYDwszMshwQZmaW5YAwM7MsB4SZmWWVFhCSrpK0StLiirYBku6R9HT6uUtql6RLJC2XtEjS/mXVZWZm1SlzBHENML5V2znAfRGxN3Bfmgb4HLB3ekwBLiuxLjMzq0JpARERc4FXWzUfB8xKz2cBEyvar43Cw0B/SYPLqs3MzDats49B7BYRLwKkn7um9iHAiop+TanNzMxqpKscpFamLbIdpSmS5kuav3r16pLLMjPrvjo7IF5u2XWUfq5K7U3A0Ip+9cDK3AoiYmZENEZEY11dXanFmpl1Z50dEHOAk9Pzk4FbK9pPSmczHQysbdkVZWZmtdGzrBVLmg0cDgyS1AScB/wIuFHS6cDzwAmp+x3ABGA5sA44tay6zMysOqUFRERMbmPWUZm+AUwtqxYzM9t8XeUgtZmZdTGljSDMzDrK8zP2q3UJXcawc5/stG15BGFmZlkOCDMzy3JAmJlZlgPCzMyyHBBmZpblgDAzsywHhJmZZTkgzMwsywFhZmZZDggzM8tyQJiZWZYDwszMshwQZmaW5YAwM7MsB4SZmWU5IMzMLMsBYWZmWTW5o5yk54A3gGZgQ0Q0ShoA3AAMB54DToyI12pRn5mZ1XYEcURENEREY5o+B7gvIvYG7kvTZmZWI11pF9NxwKz0fBYwsYa1mJl1e7UKiAD+KOkxSVNS224R8SJA+rlrjWozMzNqdAwC+HRErJS0K3CPpP+udsEUKFMAhg0bVlZ9ZmbdXk1GEBGxMv1cBdwMjAVeljQYIP1c1cayMyOiMSIa6+rqOqtkM7Nup9MDQtLHJPVreQ78X2AxMAc4OXU7Gbi1s2szM7ONarGLaTfgZkkt2/9dRNwl6VHgRkmnA88DJ9SgNjMzSzo9ICLir8DoTPsa4KjOrsfMzPK60mmuZmbWhTggzMwsywFhZmZZDggzM8tyQJiZWZYDwszMshwQZmaW5YAwM7MsB4SZmWU5IMzMLMsBYWZmWQ4IMzPLckCYmVmWA8LMzLIcEGZmluWAMDOzLAeEmZllOSDMzCzLAWFmZlkOCDMzy+pyASFpvKRlkpZLOqfW9ZiZdVddKiAk9QB+CXwOGAlMljSytlWZmXVPXSoggLHA8oj4a0S8A1wPHFfjmszMuqWuFhBDgBUV002pzczMOlnPWhfQijJt8YEO0hRgSpp8U9Ky0qvqJvaAQcArta6jSzgv90/RasX/Nit0zL/NParp1NUCogkYWjFdD6ys7BARM4GZnVlUdyFpfkQ01roOs9b8b7M2utoupkeBvSWNkNQbmATMqXFNZmbdUpcaQUTEBklfB+4GegBXRcSSGpdlZtYtdamAAIiIO4A7al1HN+Vdd9ZV+d9mDSgiNt3LzMy6na52DMLMzLoIB4T58ibWZUm6StIqSYtrXUt35IDo5nx5E+virgHG17qI7soBYb68iXVZETEXeLXWdXRXDgjz5U3MLMsBYZu8vImZdU8OCNvk5U3MrHtyQJgvb2JmWQ6Ibi4iNgAtlzd5CrjRlzexrkLSbOAvwL6SmiSdXuuauhN/k9rMzLI8gjAzsywHhJmZZTkgzMwsywFhZmZZDggzM8tyQJiZWZYDopuRNFDSwvR4SdILFdO9W/W9W1K/WtXamqSHJDVk2reoTkkjJT0h6XFJw9vo01PS623M+62kie2s/9uS+lSxnqmSvtzOeo6WdEt7r6UzSDpDUkj6bEXbCaltYpq+WtK+m7ne4yVN6+h67aPrcrcctXJFxBqgAUDSdODNiPhpZR9JoviOzLjOr3DzfYQ6vwDcFBE/7Mh6KnwbuApY316niPhlSdsvw5PAZODBND0JeKJlZkScurkrjIibO6Y062geQRgAkvaStFjS5cACYHD65mr/NP9USYvSX9xXp7bdJP1B0nxJ8yQdnNovkDRL0v2SnpZ0WmofkkYBC9O2Dmmjlp6SfiPpydTvm63m90h/vU9P002S+le8hislLZF0Z8tf8JltHEvxDfKvSbo3tf1zWn6xpG9kltlO0q8kLZV0GzConffzn4Bdgf9sWX9q/1F6D/8iadeK9+tb6fk+kv6U+ixoPbKRdFBLe1ruSkkPSvqrpKkV/U5Ov5OFqebt2npfJf1Tek1PSPptW68peQA4JK1rJ2AY8P7NfFpGeZuzrTQyuTg9/62kX0j6r/Sajk/tPSRdnn6vt0m6S+2M3qxjeARhlUYCp0bE1wCKgQRIGg18FzgkIl6VNCD1vwT4SUQ8nD7Ibgc+mebtBxwC7AQskPQfwFeA2yLixypuVNS3jToOAAZFxH5p+/0r5vUEfgcsiIgfZ5bdF5gcEU9K+gMwkeIeFx8QEXMkjQVeiYiL0/MvU9wfowcwT9KDwNKKxb4EjEivcfc07/LcC4iIiyR9BzgsIl6X1BPYGXgwIs6R9HPgNOBHrRadDUyPiNtSuG0H7JXeh8OAi4BjI6Ip/X72AY4C+gNPqQj4TwDHU/y+NkiaSfGX/jNtvK//DOwREe+0eq9z3qMIiaOB3YBb0vZaa+t3WM22dgU+TfFv6EbgZuAEisvQ7wf8DcVlYbLvvXUcjyCs0jMR8Wim/Ujghoh4FaDlJ8WHxOWSFlJ8UOwiqeVD/5aIWB8Rq4C5wIEUFwY8Q9J5wCcj4s026lhOce2dX0gaB6ytmHclbYcDFDc/ejI9fwwYvonX3OIw4PcRsS4i3kiv59BWfT4DzI6I9yKiieKDcnO8FRF3tlWbpF0oPlRvA0jv37o0+5PAr4C/T9tucXtEvJPe51eBOorfy4HA/PS7+SywJ22/r0uA36o4DvJuFa/jeorAmUQmfJOPsq1borCIjfcmOZTiOmHvRcRKNu7ishI5IKzS/7bRLvL3iBAwNiIa0mNIRLyV5rXuHxHxJ+Bw4EXgOrVxYDYdJ/k74CHgm8C/Vcz+M3CUpO3bqPXtiufNVD9Kzt0XI1telf1y3ql43lZtba1/ZVq+9UH63OsVcFXF72XfiPhhO+/rOIq/xsdShEqPTbyOvwD7AztFxDO5Dh9xW5WvSa1+WidyQFg17gUmtexaqtjFdC9Qud+78sNroqTtJQ2i+Ot8vqQ9gJciYibFvYbH5DYmqY7iIPm/A+dRfBi1mJm2e33abdNR5gLHS+oraUeK267+Z6bPpLQ/fwjFX+bteQOo+uyqiHgNeEXSMQCS+kjaIc1+Ffh74CdpV1N77gVOTO99y5lrw3Lva/qArk/hPY1iBLJDWytOdQbwPeD7bfXpqG1VeAj4kgqDKUZzVjIfg7BNiohFkn4CzJW0gWL3yOkU4XCZpFMp/i3dz8bAeBS4k+JmROdFxMsqDlZ/W9K7wJsUxyRyhgJXqtjJHhTHPyrr+YmkC4FrJJ3UQa9xnopLS7fsYrssHceo/D9yE3AExUHZZRSB0Z6ZwL2SVgDjqyzly8C/pdf3DvDFihpfVHFw/Y72Xneq+/y07e0oduV8jWKE0fp97Qn8TsVpwtsBP0672NoVEf+xiS6532F2Wy3HujbhRopdnS3v/SN8cNejlcCX+7YOJ+kC0sHfWtdi2w5JO0bEm2l08ghwUESsrnVd2zKPIMxsa3FnOrW2F8Wo1OFQMo8grKYkzefDf6j8Q0QszfXfwm1cDhzcqvnnEXFtB61/DsX3ASqdHRH35vp3dZLOoPiOSKW5EfHNXH/bdjkgzMwsy2cxmZlZlgPCzMyyHBBmZpblgDAzsywHhJmZZf1/ghDaRNNOWUIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "#color = sns.color_palette()\n",
    "\n",
    "%matplotlib inline\n",
    "sns.countplot(x=\"Triceps_skin_fold_thickness_Missing\", hue=\"Target\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9756a90>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#缺失值比较多，干脆就开一个新的字段，表明是缺失值还是不是缺失值\n",
    "train['serum_insulin_Missing'] = train['serum_insulin'].apply(lambda x: 1 if pd.isnull(x) else 0)\n",
    "sns.countplot(x=\"serum_insulin_Missing\", hue=\"Target\",data=train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x9943c18>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x648 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#特征间的相关性\n",
    "data_corr = train.corr().abs()\n",
    "\n",
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_corr,annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_train = train['Target']\n",
    "X_train = train.drop([\"Target\"], axis=1)\n",
    "\n",
    "#保存特征名字以备后用（可视化）\n",
    "feat_names = X_train.columns \n",
    "\n",
    "#sklearn的学习器大多之一稀疏数据输入，模型训练会快很多\n",
    "#查看一个学习器是否支持稀疏数据，可以看fit函数是否支持: X: {array-like, sparse matrix}.\n",
    "#可自行用timeit比较稠密数据和稀疏数据的训练时间\n",
    "from scipy.sparse import csr_matrix\n",
    "X_train = csr_matrix(X_train)\n",
    "\n",
    "#去除nan数据，需要用这个函数，否则会报错\n",
    "from math import isnan\n",
    "\n",
    "for i in range(0, 768):\n",
    "    for j in range(0, 10):\n",
    "        if (isnan(X_train[i,j])):\n",
    "            X_train[i,j] = 0.0\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logloss of each fold is:  [0.50576071 0.51467004 0.49756256 0.45717885 0.4915356 ]\n",
      "cv logloss is: 0.49334155085778486\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "lr = LogisticRegression()\n",
    "\n",
    "\n",
    "# 交叉验证用于评估模型性能和进行参数调优（模型选择）\n",
    "#采用5折交叉验证\n",
    "from sklearn.model_selection import cross_val_score\n",
    "loss1 = cross_val_score(lr, X_train, y_train, cv=5, scoring='neg_log_loss')\n",
    "print ('logloss of each fold is: ',-loss1)\n",
    "print ('cv logloss is:', np.mean(-loss1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy of each fold is:  [0.76623377 0.74675325 0.75974026 0.81045752 0.75816993]\n",
      "cv accuracy is: 0.768270944741533\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "loss2 = cross_val_score(lr, X_train, y_train, cv=5, scoring='accuracy')\n",
    "print ('accuracy of each fold is: ',loss2)\n",
    "print ('cv accuracy is:', np.mean(loss2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "正则超参数调优"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#设置搜索参数\n",
    "penaltys = ['11','12']\n",
    "Cs = [0.1, 1, 10, 100, 1000]\n",
    "tuned_parameters = dict(penaltys, C = Cs)\n",
    "lr_penalty = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Parameter values for parameter (1) need to be a sequence(but not a string) or np.ndarray.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-f3f1cb066d5a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m#采用负log损失作为评价指标\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# 此处不知道为什么报错？\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mgrid1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGridSearchCV\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlr_penalty\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtuned_parameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'neg_log_loss'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mgrid1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, estimator, param_grid, scoring, fit_params, n_jobs, iid, refit, cv, verbose, pre_dispatch, error_score, return_train_score)\u001b[0m\n\u001b[0;32m   1076\u001b[0m             return_train_score=return_train_score)\n\u001b[0;32m   1077\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparam_grid\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparam_grid\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1078\u001b[1;33m         \u001b[0m_check_param_grid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1079\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1080\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_param_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py\u001b[0m in \u001b[0;36m_check_param_grid\u001b[1;34m(param_grid)\u001b[0m\n\u001b[0;32m    355\u001b[0m                 raise ValueError(\"Parameter values for parameter ({0}) need \"\n\u001b[0;32m    356\u001b[0m                                  \u001b[1;34m\"to be a sequence(but not a string) or\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 357\u001b[1;33m                                  \" np.ndarray.\".format(name))\n\u001b[0m\u001b[0;32m    358\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    359\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mv\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Parameter values for parameter (1) need to be a sequence(but not a string) or np.ndarray."
     ]
    }
   ],
   "source": [
    "#采用负log损失作为评价指标\n",
    "# 此处不知道为什么报错？\n",
    "grid1 = GridSearchCV(lr_penalty, tuned_parameters, cv=5, scoring='neg_log_loss')\n",
    "grid1.fit(X_train,y_train)"
   ]
  }
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